Machine Learning Engineer

Who you are

• Have strong experience in Machine Learning with solid foundations in the ML lifecycle, evaluation methodologies, and statistical thinking
• Have hands-on experience with GenAI, Large Language Models, and NLP techniques, including RAG and agent-based systems
• Are proficient in Python and common ML libraries (e.g. PyTorch, scikit-learn, XGBoost, Pandas)
• Have experience deploying and operating ML or AI systems in production environments
• Understand ML Ops and DevOps principles (e.g. Docker, CI/CD, infrastructure as code)
• Have experience working with cloud infrastructure (preferably AWS)
• Take ownership of technical tasks and proactively drive solutions forward

What the job involves

You’ll join a cross-functional team of Machine Learning Engineers and Data Engineers working on some of our most exciting GenAI initiatives

We’re building agentic systems such as our Travel Assistant, an AI-powered experience that helps customers with refunds, journey updates, and ticket conditions and expanding into Voice AI agents to intelligently handle and redirect customer queries

This is a team that combines strong ML fundamentals with cutting-edge LLM frameworks (LangGraph, LangChain), RAG architectures, MCP integrations, and production monitoring tools like Braintrust, all deployed on AWS

We value ownership, curiosity, and thoughtful experimentation in a healthy, supportive environment

This role is focused on designing, building, and operating LLM-powered and predictive ML systems in production

You’ll work on agentic AI systems, including our Travel Assistant and Voice AI initiatives, while also contributing to more traditional ML services that the team owns

If you’re passionate about GenAI, LLMs, NLP, and shipping robust ML systems end-to-end, this is an opportunity to work at the forefront of applied AI in a high-impact consumer platform

• Design and build LLM-powered agentic systems using frameworks such as LangGraph and LangChain
• Develop and optimise RAG pipelines, tool-using agents, and multi-step workflows with appropriate guardrails and validation
• Monitor and evaluate model and agent performance using appropriate ML metrics (e.g. precision, recall) and production monitoring tools
• Maintain and improve traditional ML models alongside newer GenAI capabilities
• Partner closely with stakeholders to frame problems, define success metrics, and deliver measurable business impact
• Take ownership of technical initiatives, driving delivery from ideation through to production and iteration
• Contribute to our wider AI & ML community through knowledge sharing, experimentation, and continuous learning